Workers in the field of computer vision have long been interested in detecting features in images for purposes of image classification.
Among the many applications of image classification is character recognition. Character recognition can be posed as an image classification problem in which an image of the character to be classified is processed to determine, in accordance with a predetermined criterion, which character it represents.
Another application of image classification is feature detection and discrimination, in which an input image is analyzed to determine whether a predetermined feature is present. If the feature is found in the image, it can be used to discriminate that image from others not containing the feature.
Image classification schemes are typically developed through a trial and error procedure involving testing the schemes on a selected collection of training images to predict their performance when used on input images which have not been preselected. This trial and error procedure is iterative, and can involve a human being who uses the results achieved by the most recently proposed classification scheme to propose another classification scheme. The iterative procedure terminates when the person developing the scheme is satisfied with the performance achieved.
An especially useful computer organization for the purposes of image analysis is the serial neighborhood processor. One form of the serial neighborhood processor is the one disclosed in U.S. Pat. No. 4,167,728 to Sternberg, which modifies an input image in a series of steps. At every step of the series, each pixel can be modified according to the state of the corresponding pixel and its neighboring pixels in the preceding image. The changes imposed at each step in the series can be described in terms of morphological transformations such as erosion and dilation, and spatial constants called structuring elements. The structuring elements are used to describe which neighboring image points contribute to the resultant image.
The development of a pattern classification scheme involving neighborhood processing steps, therefore, involves, at each stage, a choice of the morphological transformation to be used and the structuring element to be used with it. Experience has shown that, while humans are adept at choosing the morphological transformation to be used, the process of choosing the structuring element to be used with the transformation is tedious and non-intuitive. Computers are especially useful in choosing the structuring elements.
It is therefore desirable to have a method for generating the structuring elements to be used by a serial neighborhood processor to perform a pattern classification task. These programs include a preselected series of morphological transformations and the generated structuring elements used by these morphological transformations.